The present invention relates to the field of real-time position detection and motion tracking of wireless communications devices.
Communications with wireless devices has quickly become a ubiquitous part of modern life. Such wireless devices can take any of a number of forms. As examples, wireless devices may include cellular telephones and pagers, as well as various types of Internet, Web, or other network enabled devices, such as personal digital assistants (PDAs). Rapid growth has come in the mobile telephone realm and in the realm of other personal and business computing devices. The number of cellular telephone customers, for example, has grown exponentially over the past few years, so too has the number of wireless personal and business computing devices. Any of these network enabled devices may include Internet or Web functionality. Generally, a wireless device configured for transmitting, receiving, accessing, or exchanging data via a network may be referred to as a xe2x80x9cmobile devicexe2x80x9d and communications between mobile devices may be referred to as xe2x80x9cwireless communicationsxe2x80x9d.
As part of the technical development of the networks to meet the demand for mobile communications, carriers have migrated from an analog-based technology to several digital transport technologies, wherein digital data is xe2x80x9cpacketizedxe2x80x9d and transmitted across digital networks. Newer versions of digital wireless communication networks support a variety of data communication services that are intended to extend the common data communication capabilities of the wired domain to the wireless mobile domain.
The current trend in the cellular realm is toward the Third Generation of Wireless Telephony (3G) networks (e.g., 3G-1x networks). The 3RD Generation Partnership Project 2 (3GPP2) standard entitled Wireless IP Network Standard, 3GPP2 P.S0001-A, Version 3.0.0, (copyright) 3GPP2, version date Jul. 16, 2001 (the xe2x80x9c3GPP2 Standardxe2x80x9d, a.k.a. the IS-835 Standardxe2x80x9d) codifies the use of mobile IP in a 3G-1x packet data network, also referred to as a code division multiple access (CDMA) or CMDA2000-1x packet data network.
In the personal and business realm, where wireless communication can take place in a localized area via local communications network, the IEEE 802.11 standard is prevalent. A localized area may be a building, an area within a building, an area comprising several buildings, outdoor areas, or a combination of indoor and outdoor areas. Most modern means of position detection and motion tracking techniques of an object either involve: 1) signal timing analysis, such as time (difference) of arrival (TOA or TDOA) based measurements, such as global positioning systems (GPS); 2) signal frequency shift analysis, such as RADAR; 3) the use of predetermined signal beacons for active or passive detection, such as interrupting a beam of light upon entry or exit of a space; or 4) having a network of receivers that detect presence of a mobile beacon signal traveling through a space, such as infrared transmitters on PDAs or cellular telephones within reach of local cellular tower, or triangulation via a combination of these or related methods.
Most of these techniques are application-specific to the task of measuring position and often serve no other function, except in the case of a mobile phone as noted above, where the location of a cellular phone can be detected at a coarse scale of hundreds of feet, concluding it is in the vicinity of a given tower. Some of these techniques are unavailable in certain spaces such as the use of GPS indoors or underground, or are impractical because of interference, signal multi-path effects, or because the optimal speed profiles for the objects being tracked (such as RADAR) do not match the motive behavior of the objects. Lastly, merely the deployment of a network of sensors as described above for position detection of a mobile device could be prohibitively expensive and impractical for this single function.
In some settings, detection and location within a defined local area is performed using a local area network (LAN) comprised of a set of xe2x80x9caccess pointsxe2x80x9d (APs). The APs are communication ports for wireless devices, wherein the communication occurs across an xe2x80x9cair linkxe2x80x9d between the wireless device and the APs. That is, APs pass messages received from the wireless device across the LAN to other servers, computers, applications, subsystems or systems, as appropriate. The APs are bi-directional, so also configured to transmit to the wireless device. Typically, the APs are coupled to one or more network servers, which manage the message traffic flow. Application servers may be coupled to or accessed via the network servers, to provide data or typical application functionality to the wireless device.
In such systems, the process of defining the local area (e.g., room layouts, ground layouts, and so on) to the network is often referred to as xe2x80x9ctrainingxe2x80x9d the area or system. The area is divided up into spaces, which wireless devices transition between as they migrate through the trained area. The location and detection within the trained area is typically determined as a function of the signal strength from the wireless device with respect to one or more APs. The APs are configured to determine the signal strength and pass it on to a back-end subsystem for processing.
Location and detection are typically determined as a function of received signal strength indicator (RSSI) values obtained from the communications between the wireless device and the LAN. As a general rule, the higher the signal strength, the closer a transmitting wireless device is presumed to be to an AP. Changes in the signal strength as the wireless device moves about the trained area allows for tracking. If there are at least three APs that receive the signals from the wireless device, trilateration can be used to determine the location of the device within the trained area. Trilateration is a method of determining the position of the wireless device as a function of the lengths between the wireless device and each of the APs.
Trilateration calculations are performed by the wireless device using the RSSI data, which must be configured with appropriate software (e.g., a client-side module) to accomplish such tasks. As a result, the demands on the wireless device are increased. Furthermore, while detection and tracking are desired for substantially all wireless devices within the trained area, it is much more difficult to achieve, since the many types of wireless devices may all have different configurations.
A system and method are provided that allow for network-based position detection and tracking of a wireless mobile (or client) device within a defined space, e.g., a mobile device detection and tracking system. Preferably, the mobile device needs no special client-side configuration, modules, or programs to be detected and tracked, since detection and tracking are preformed on the network side of the interface. The availability of applications and access to data may be selectively provided or inhibited as a function of the location of the mobile device and an identity of the mobile device or its user, or both. The present inventive approach to real-time position detection or motion tracking can be applied to outdoor wide-area communications media, such as cellular or pager networks or indoor/outdoor to wireless local area networks (LAN) and communications such as IEEE 802.11 or xe2x80x9cBluetoothxe2x80x9d.
The mobile device may be any known portable or transportable device configured for wireless communications, such as a mobile telephone, personal digital assistant (PDA), pager, e-mail device, laptop, or any Web enabled device. Many of such devices may be handheld devices, but other wireless devices that are not of such a compact size could also be detected and tracked. As wireless devices, the mobile devices are configured to communicate with a network through a wireless interface.
The mobile device detection and tracking system includes a network, a plurality of detectors (e.g., access points (APs)), and at least one processing system. The processing system preferably includes or supports a user interface and includes memory to facilitate the initial setup, operation, and maintenance of the system. The network couples a set of selectively distributed access points to the processing system. The network may also include or have access to a variety of functionality and data, which may be hosted on the network or on subsystems or on systems accessible via the network, possibly via another one or more networks.
The mobile device detection and tracking system combines a digital definition of the physical space with a statistical signal strength model to provide a context within which mobiles devices may be detected and tracked. The digital form or map of the physical space preferably includes the identification of permanent obstructions that will effect the transmission and reception capabilities of the access points, e.g., walls, columns, and so on. The signal strength model defines, for each access point within the physical space, a pattern of signal strength reception that is anticipated from a mobile device transmitting within the space, taking into account the obstructions and placement of the access points. With a plurality of access points, a plurality of signal strength patterns will be defined, several of which will, typically, overlap to some extent.
The defined space is comprised of a set of defined regions, areas or locations (collectively referred to as xe2x80x9clocalesxe2x80x9d). A locale may be defined as an interior or exterior space or location, or a combination thereof. For example, a conference room may be defined as a locale. Each locale is defined within the system in relationship to the digital form of the physical space. Locales may be defined either prior to or after generation of the signal strength model. Typically, once the digital form of the space is formed, the locales are defined and the statistical signal strength model is then defined. In other forms, an iterative process of defining locales, generating the signal strength model, and (optionally) positioning the access points may be used.
With the digital form of the physical space defined, the signal strength model can be determined. The process of generating a signal strength model is referred to as xe2x80x9ctrainingxe2x80x9d the area or system. In accordance with the present invention, the signal strength model can be created in one of at least two manners. In a first manner, access points are installed in the physical space and actual signal strength data is collected through migration of a transmitting mobile device through the space. The actual signal strength data received from the access points are used to build a statistical signal strength model associated with the digital form of the physical space. Any one or more of a variety of known statistical modeling approaches may be used to build the signal strength model, such as a Markov model.
A second manner of building the statistical model includes using simulated access points and simulated mobile device readings within the context of the digital form representation of the physical space. In such a case, the system assumes certain reception and transmission characteristics of the access points and of the mobile devices within the context of the space in digital form. The statistical signal strength model is generated as a function of these assumptions. Preferably, the system allows for editing the assumptions (including the positioning of obstructions and access points) to yield different statistical models using the user interface of the system.
Accordingly, in some forms, the mobile device detection and tracking system may include a module for determining the placement of the access points within the defined space. In such a case, the space in digital map is defined, including a definition of the obstructions. Obstructions may be assigned values relating to the amount of interference they tend to provide. For example, a brick wall typically provides a greater amount of interference than does a window. Analyzing the interference characteristics in light of a range of signal strengths from a foreseeable set of mobile devices and in light of the detection and transmission characteristics of the access points, allows access point placement to be determined. If there are detectors having different detection and transmission characteristics identified in the system, the system may not only determine placement, but also selection of detectors. In some forms, the system may also determine placement of the detectors with respect to the locales.
With the defined space having been trained, position detection and motion tracking are accomplished within and among the locales by processing actual signal strength data of a mobile device as it moves about or resides in the defined space, and comparing the actual data against the known statistical signal strength model. At any one time, a mobile device transmitting in the trained space may be detected by a plurality of detectors, which may be in the same or different locales. A comparison of the actual signal strength data at each access point receiving the mobile device""s signal with the signal strength patterns (included in the signal strength model) of those access points allow for a determination of the real-time location of the mobile device within the defined space. Such analysis, when performed overtime, allows tracking of the mobile device within and among the locales.
To improve the accuracy and reliability of tracking, the concept of locale adjacency may be used. That is, if a locale xe2x80x9cAxe2x80x9d is only adjacent to a locale xe2x80x9cBxe2x80x9d and a locale xe2x80x9cCxe2x80x9d and, according to signal strength data, the mobile device could be in locale B or a locale xe2x80x9cExe2x80x9d, knowing that the previous locale of the mobile device was locale A allows the system to accurately determine that the mobile device is currently in local B, and not locale E.
The concept of adjacency may be implemented in a state-based approach. In such a case, each locale may be uniquely modeled as state within a state diagram. Since only a finite number of known next states and previous states can exist for each state, a current state can be determined with greater reliability given knowledge of the previous state and its subset of allowable next states.
In various forms of the present invention, a combination of approaches may be implemented to locate and track a mobile device through the defined space and from locale to locale. For example, using clustering statistics of received signal strength indicator (RSSI) data from one or more access points, a determination of the location of the mobile device can be made with relatively high accuracy. Additionally, a trilateration analysis of RSSI data received from three different detectors can be performed, wherein the location of the mobile device can be determined as a function of the length of the sides of a triangle formed by the three access points. The results of the clustering statistics and the trilateration are combined to increase the accuracy of the overall determination of the location of the mobile device. This approach can also be performed over time for improved tracking.
Various forms of the present invention may include a feedback subsystem or monitor that monitors the status of the access points. For instance, such a subsystem may be configured to determine if an access point is malfunctioning, turned off or inoperable, if a new access point has been added, or some combination of the foregoing. In such a form, a feedback path is provided between the access points and a monitoring processor, manager, module, program, or subsystem (collectively xe2x80x9cmonitoring modulexe2x80x9d). The monitoring module obtains status data provided by each access point, which is used for the above determinations, and produces status messages, error messages or both. The messages may come, as an example, in the form of an e-mail or a telephone alert to a network administrator, technician, manager, security personnel, or some combination thereof. In some forms, a system and method in accordance with the present invention may adjust the statistical model in response to loss or malfunctioning of one or more access points.